Client Report - Project 0: Introduction

Table of contents

• QUESTION|TASK 1
• QUESTION|TASK 2

import pandas as pd
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
from palmerpenguins import load_penguins
df = load_penguins()

QUESTION|TASK 1

Include the tables created from PY4DS: CH2 Data Visualization used to create the above chart

This is a chart that shows just the first and last five entries of the penguins.

penguins = load_penguins()
penguins

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007
...	...	...	...	...	...	...	...	...
339	Chinstrap	Dream	55.8	19.8	207.0	4000.0	male	2009
340	Chinstrap	Dream	43.5	18.1	202.0	3400.0	female	2009
341	Chinstrap	Dream	49.6	18.2	193.0	3775.0	male	2009
342	Chinstrap	Dream	50.8	19.0	210.0	4100.0	male	2009
343	Chinstrap	Dream	50.2	18.7	198.0	3775.0	female	2009

344 rows × 8 columns

---

With the tag “.info()”, we can see a summary of the chart. It makes the info easier to read at a glance.

penguins.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 8 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   species            344 non-null    object 
 1   island             344 non-null    object 
 2   bill_length_mm     342 non-null    float64
 3   bill_depth_mm      342 non-null    float64
 4   flipper_length_mm  342 non-null    float64
 5   body_mass_g        342 non-null    float64
 6   sex                333 non-null    object 
 7   year               344 non-null    int64  
dtypes: float64(4), int64(1), object(3)
memory usage: 21.6+ KB

---

QUESTION|TASK 2

Recreate the example charts from PY4DS: CH2 Data Visualization of the textbook.

Now this is us taking the data from the penguins and compairing Body Mass with Flipper Length. As you see, the info is changed from a chart to a dotted graph.

ggplot(data=penguins, mapping=aes(x="flipper_length_mm", y="body_mass_g")
) + geom_point()

---

When we add color on the graph, we can see that there are three different types of Penguin Species that we charted. The Adelie tend to have less Body Mass along side with the Chinstrap. The Gentoo have more Body mass and Larger flippers.

ggplot(
  data = penguins, 
  mapping = aes(x = "flipper_length_mm", y = "body_mass_g")
  ) + geom_point(mapping = aes(color = "species")) 

---

When we smooth out the averages, we can see the average of each point of each species. While it looked like Green was farther to the left of Red, we can see that the average line of Red is more than the average line of green.

ggplot(
  data=penguins,
  mapping=aes(x="flipper_length_mm", y="body_mass_g", color="species"),
  ) + geom_point(
  ) + geom_smooth(method="lm")

---

With finding the average of the Species, we can see the line that helps us understand the data.

ggplot(
  data=penguins, 
  mapping=aes(x="flipper_length_mm", y="body_mass_g")
  ) + geom_point(mapping=aes(color="species")
  ) + geom_smooth(method="lm")

---

This one was to practice changing the species dots into different symbols. Can help people who may be color blind or differentiate the different points.

ggplot(data=penguins, mapping=aes(x="flipper_length_mm", y="body_mass_g")
  ) + geom_point(mapping=aes(color="species", shape="species")
  ) + geom_smooth(method="lm")